Literature DB >> 20048384

Multiple testing and its applications to microarrays.

Yongchao Ge1, Stuart C Sealfon, Terence P Speed.   

Abstract

The large-scale multiple testing problems resulting from the measurement of thousands of genes in microarray experiments have received increasing interest during the past several years. This article describes some commonly used criteria for controlling false positive errors, including familywise error rates, false discovery rates and false discovery proportion rates. Various statistical methods controlling these error rates are described. The advantages and disadvantages of these methods are discussed. These methods are applied to gene expression data from two microarray studies and the properties of these multiple testing procedures are compared.

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Year:  2009        PMID: 20048384      PMCID: PMC4131454          DOI: 10.1177/0962280209351899

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  13 in total

1.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Authors:  B M Bolstad; R A Irizarry; M Astrand; T P Speed
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

2.  Controlling the proportion of false positives in multiple dependent tests.

Authors:  R L Fernando; D Nettleton; B R Southey; J C M Dekkers; M F Rothschild; M Soller
Journal:  Genetics       Date:  2004-01       Impact factor: 4.562

3.  The 'miss rate' for the analysis of gene expression data.

Authors:  Jonathan Taylor; Robert Tibshirani; Bradley Efron
Journal:  Biostatistics       Date:  2005-01       Impact factor: 5.899

4.  False discovery rate, sensitivity and sample size for microarray studies.

Authors:  Yudi Pawitan; Stefan Michiels; Serge Koscielny; Arief Gusnanto; Alexander Ploner
Journal:  Bioinformatics       Date:  2005-04-19       Impact factor: 6.937

5.  Multiple testing. Part II. Step-down procedures for control of the family-wise error rate.

Authors:  Mark J van der Laan; Sandrine Dudoit; Katherine S Pollard
Journal:  Stat Appl Genet Mol Biol       Date:  2004-06-14

6.  Multiple testing. Part I. Single-step procedures for control of general type I error rates.

Authors:  Sandrine Dudoit; Mark J van der Laan; Katherine S Pollard
Journal:  Stat Appl Genet Mol Biol       Date:  2004-06-09

7.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

8.  Microarray expression profiling identifies genes with altered expression in HDL-deficient mice.

Authors:  M J Callow; S Dudoit; E L Gong; T P Speed; E M Rubin
Journal:  Genome Res       Date:  2000-12       Impact factor: 9.043

9.  Use of a cDNA microarray to analyse gene expression patterns in human cancer.

Authors:  J DeRisi; L Penland; P O Brown; M L Bittner; P S Meltzer; M Ray; Y Chen; Y A Su; J M Trent
Journal:  Nat Genet       Date:  1996-12       Impact factor: 38.330

10.  Expression monitoring by hybridization to high-density oligonucleotide arrays.

Authors:  D J Lockhart; H Dong; M C Byrne; M T Follettie; M V Gallo; M S Chee; M Mittmann; C Wang; M Kobayashi; H Horton; E L Brown
Journal:  Nat Biotechnol       Date:  1996-12       Impact factor: 54.908

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  5 in total

1.  Pathway crosstalk effects: Shrinkage and disentanglement using a Bayesian hierarchical model.

Authors:  Alin Tomoiaga; Peter Westfall; Michele Donato; Sorin Draghici; Sonia Hassan; Roberto Romero; Paola Tellaroli
Journal:  Stat Biosci       Date:  2016-07-26

2.  On Permutation Procedures for Strong Control in Multiple Testing with Gene Expression Data.

Authors:  Grzegorz A Rempala; Yuhong Yang
Journal:  Stat Interface       Date:  2013       Impact factor: 0.582

3.  Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data.

Authors:  Karin Schork; Katharina Podwojski; Michael Turewicz; Christian Stephan; Martin Eisenacher
Journal:  Methods Mol Biol       Date:  2021

4.  An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

Authors:  Derrick K Rollins; Ailing Teh
Journal:  BioData Min       Date:  2010-12-17       Impact factor: 2.522

Review 5.  Progress Toward a Multiomic Understanding of Traumatic Brain Injury: A Review.

Authors:  Philip A Kocheril; Shepard C Moore; Kiersten D Lenz; Harshini Mukundan; Laura M Lilley
Journal:  Biomark Insights       Date:  2022-06-13
  5 in total

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